Skip to main content
SpringerLink
Log in

Paraphrasing and prediction with self-explanation as generative strategies for learning science principles in a simulation

  • Research Article
  • Published:
Educational Technology Research and Development Aims and scope Submit manuscript

Abstract

This study examined the incorporation of generative strategies for the guided discovery of physics principles in a simulation. Participants who either paraphrased or predicted and self-explained guided discovery assignments exhibited improved performance on an achievement test as compared to a control group. Calibration accuracy (the correspondence between judgments of performance and actual performance) was also improved for the two generative strategy groups. The thoroughness of generative content and quality of self-explanations significantly predicted test performance. In regards to cognitive load, participants who predicted and self-explained reported significantly higher levels of mental effort, decreased levels of confidence, and higher levels of frustration compared to those in other treatments. The improvement in learning by the two generative strategy groups is consistent with the generative model of learning describing the importance of knowledge construction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Afflerbach, P. (1990). The influence of prior knowledge and text genre on readers’ prediction strategies. Journal of Literacy Research, 22(2), 131–148.

    Article  Google Scholar 

  • Afflerbach, P., & Walker, B. (1990). Prediction instruction in basal readers. Reading Research and Instruction, 29(4), 26–45. doi:10.1080/19388079009558022.

    Google Scholar 

  • Alessi, S., & Trollip, S. R. (2001). Multimedia for learning: Methods and development (3rd ed.). Boston, MA: Allyn & Bacon.

    Google Scholar 

  • Alfieri, L., Brooks, P. J., Aldrich, N. J., & Tenenbaum, H. R. (2011). Does discovery-based instruction enhance learning? Journal of Educational Psychology, 103(1), 1–18. doi:10.1037/a0021017.

    Article  Google Scholar 

  • Anderson, R. C., & Pearson, P. D. (1984). A schema-theoretic view of basic processes in reading comprehension. In P. D. Pearson, R. Barr, M. L. Kamil, & P. Modenthal (Eds.), Handbook of reading research (Vol. 1, pp. 255–291). White Plains, NY: Longman.

    Google Scholar 

  • Bangert-Drowns, R. L., Kulik, J., & Kulik, C. (1985). Effectiveness of computer-based education in secondary schools. Journal of Computer-Based Instruction, 12, 59–68.

    Google Scholar 

  • Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-regulated learning: Beliefs, techniques, and illusions. Annual Review of Psychology, 64, 417–444.

    Article  Google Scholar 

  • Bol, L., & Garner, J. (2011). The challenges of supporting self-regulation in distance education environments. Journal of Computing in Higher Education, 23(2–3), 104–123.

    Article  Google Scholar 

  • Bol, L., & Hacker, D. J. (2001). A comparison of the effects of practice tests and traditional review on performance and calibration. The Journal of Experimental Education, 69(2), 133–151. doi:10.1080/00220970109600653.

    Article  Google Scholar 

  • Bol, L., Hacker, D. J., O’Shea, P., & Allen, D. (2005). The influence of overt practice, achievement level, and explanatory style on calibration accuracy and performance. The Journal of Experimental Education, 73(4), 269–290.

    Article  Google Scholar 

  • Bol, L., Hacker, D. J., Walck, C. C., & Nunnery, J. A. (2012). The effects of individual or group guidelines on the calibration accuracy and achievement of high school biology students. Contemporary Educational Psychology, 37(4), 280–287. doi:10.1016/j.cedpsych.2012.02.004.

    Article  Google Scholar 

  • Bretzing, B. H., & Kulhavy, R. W. (1979). Notetaking and depth of processing. Contemporary Educational Psychology, 4(2), 145–153. doi:10.1016/0361-476X(79)90069-9.

    Article  Google Scholar 

  • Byrne, M. D., Catrambone, R., & Stasko, J. T. (1999). Evaluating animations as student aids in learning computer algorithms. Computers & Education, 33(4), 253–278. doi:10.1016/S0360-1315(99)00023-8.

    Article  Google Scholar 

  • Chi, M. T. H. (2009). Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in Cognitive Science, 1, 73–105. doi:10.1111/j.1756-8765.2008.01005.x.

    Article  Google Scholar 

  • Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145–182. doi:10.1016/0364-0213(89)90002-5.

    Article  Google Scholar 

  • Chi, M. T. H., De Leeuw, N., Chiu, M.-H., & Lavancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18(3), 439–477. doi:10.1016/0364-0213(94)90016-7.

    Google Scholar 

  • Chi, M. T. H., & VanLehn, K. A. (1991). The content of physics self-explanations. The Journal of the Learning Sciences, 1(1), 69–105.

    Article  Google Scholar 

  • Clark, D., & Linn, M. C. (2003). Designing for knowledge integration: The impact of instructional time. The Journal of the Learning Sciences, 12(4), 451–493.

    Article  Google Scholar 

  • Collins, A., Brown, J. S., & Larkin, K. M. (1980). Inferences in text understanding. In R. J. Spiro, B. C. Bruce, & W. F. Brewer (Eds.), Theoretical issues in reading comprehension (pp. 385–407). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers.

    Google Scholar 

  • Craik, F. I. M., & Lockhart, R. S. (1972). Levels of processing: A framework for memory research. Journal of Verbal Learning and Verbal Behavior, 11(6), 671–684.

    Article  Google Scholar 

  • Davey, B., & McBride, S. (1986). Effects of question-generation training on reading comprehension. Journal of Educational Psychology, 78(4), 256–262. doi:10.1037/0022-0663.78.4.256.

    Article  Google Scholar 

  • de Bruin, A. B. H., Rikers, R. M. J. P., & Schmidt, H. G. (2007). The effect of self-explanation and prediction on the development of principled understanding of chess in novices. Contemporary Educational Psychology, 32(2), 188–205. doi:10.1016/j.cedpsych.2006.01.001.

    Article  Google Scholar 

  • de Jong, T. (2006). Scaffolds for scientific discovery learning. In J. Elen & R. E. Clark (Eds.), Handling complexity in learning environments: Theory and research (pp. 107–128). London: Elsevier.

    Google Scholar 

  • de Jong, T., & van Joolingen, W. R. (1998). Scientific discovery learning with computer simulations of conceptual domains. Review of Educational Research, 68(2), 179–201. doi:10.3102/00346543068002179.

    Article  Google Scholar 

  • Freeman, R. H. (1982). Improving the comprehension of stories using predictive strategies. Paper presented at the Annual Meeting of the International Reading Association, Chicago, IL.

  • Gerjets, P., Scheiter, K., & Catrambone, R. (2006). Can learning from molar and modular worked examples be enhanced by providing instructional explanations and prompting self-explanations? Learning and Instruction, 16(2), 104–121. doi:10.1016/j.learninstruc.2006.02.007.

    Article  Google Scholar 

  • Glover, J. A., Plake, B. S., Roberts, B., Zimmer, J. W., & Palmere, M. (1981). Distinctiveness of encoding: The effects of paraphrasing and drawing inferences on memory from prose. Journal of Educational Psychology, 73(5), 736–744. doi:10.1037/0022-0663.73.5.736.

    Article  Google Scholar 

  • Glover, J. A., Timme, V., Deyloff, D., Rogers, M., & Dinell, D. (1987). Oral directions: Remembering what to do when. Journal of Educational Research, 81(1), 33–40.

    Article  Google Scholar 

  • Grimes, P. W. (2002). The overconfident principles of economics students: An examination of a metacognitive skill. The Journal of Economic Education, 33, 15–30.

    Article  Google Scholar 

  • Hacker, D. J., Bol, L., & Bahbahani, K. (2008). Explaining calibration accuracy in classroom contexts: The effects of incentives, reflection, and explanatory style. Metacognition and Learning, 3(2), 101–121. doi:10.1007/s11409-008-9021-5.

    Article  Google Scholar 

  • Hacker, D. J., Bol, L., Horgan, D. D., & Rakow, E. A. (2000). Test prediction and performance in a classroom context. Journal of Educational Psychology, 92(1), 160–170. doi:10.1037/0022-0663.92.1.160.

    Article  Google Scholar 

  • Hansen, J. (1981). The effects of inference training and practice on young children’s reading comprehension. Reading Research Quarterly, 16(3), 391–417.

    Article  Google Scholar 

  • Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of experimental and theoretical research. In P. A. Hancock & N. Meshkati (Eds.), Human mental workload (pp. 139–183). Amsterdam: North Holland.

    Chapter  Google Scholar 

  • Hegarty, M., Kriz, S., & Cate, C. (2003). The roles of mental animations and external animations in understanding mechanical systems. Cognition and Instruction, 2(4), 325–360.

    Google Scholar 

  • Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark (2006). Educational Psychologist, 42(2), 99–107. doi:10.1080/00461520701263368.

    Article  Google Scholar 

  • Jonassen, D. H. (1988). Integrating learning strategies into courseware to facilitate deeper processing. In D. Jonassen (Ed.), Instructional designs for microcomputer courseware (pp. 151–181). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Jonassen, D. H., & Ionas, I. G. (2008). Designing effective supports for causal reasoning. Educational Technology Research and Development, 56, 287–308. doi:10.1007/s11423-006-9021-6.

    Article  Google Scholar 

  • Kasmer, L., & Kim, O.-K. (2011). Using prediction to promote mathematical understanding and reasoning. School Science and Mathematics, 111(1), 20–33.

    Article  Google Scholar 

  • Keren, G. (1991). Calibration and probability judgements: Conceptual and methodological issues. Acta Psychologica, 77(3), 217–273. doi:10.1016/0001-6918(91)90036-Y.

    Article  Google Scholar 

  • Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86. doi:10.1207/s15326985ep4102_1.

    Article  Google Scholar 

  • Kulik, C.-L. C., & Kulik, J. A. (1991). Effectiveness of computer-based instruction: An updated analysis. Computers in Human Behavior, 7(1–2), 75–94. doi:10.1016/0747-5632(91)90030-5.

    Article  Google Scholar 

  • Linn, M. C., & Hsi, S. (2000). Computers, teachers, peers: Science learning partners. Mahwah, NJ: Lawrence Erlbaum Associates Inc.

    Google Scholar 

  • Maki, R. H., Foley, J. M., Kajer, W. K., Thompson, R. C., & Willert, M. G. (1990). Increased processing enhances calibration of comprehension. Journal of Experimental Psychology. Learning, Memory, and Cognition, 16(4), 609–616. doi:10.1037/0278-7393.16.4.609.

    Article  Google Scholar 

  • Markle, S. M. (1969). Good frames and bad: A grammar of frame writing. New York: Wiley.

    Google Scholar 

  • Mayer, R. E. (2004). Should there be a three strikes rule against pure discovery learning? The case for guided methods of instruction. American Psychologist, 59, 14–19.

    Article  Google Scholar 

  • McNamara, D. S., & Magliano, J. P. (2009). Self-explanation and metacognition: The dynamics of reading. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 60–82). New York: Routledge.

    Google Scholar 

  • Morrison, G. R., & Anglin, G. J. (2005). Research on cognitive load theory: Application to e-learning. Educational Technology Research and Development, 53(3), 94–104.

    Article  Google Scholar 

  • Okada, T., & Simon, H. A. (1997). Collaborative discovery in a scientific domain. Cognitive Science, 21(2), 109–146. doi:10.1207/s15516709cog2102_1.

    Article  Google Scholar 

  • Palincsar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and monitoring activities. Cognition & Instruction, 1, 117–175.

    Article  Google Scholar 

  • Posner, G. J., Strike, K. A., Hewson, P. W., & Gertzog, W. A. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change. Science Education, 66, 211–227.

    Article  Google Scholar 

  • Pressley, M., & Ghatala, E. S. (1990). Self-regulated learning: Monitoring learning from text. Educational Psychologist, 25, 19–33.

    Article  Google Scholar 

  • Reigeluth, C. M., & Schwartz, E. (1989). An instructional theory for the design of computer-based simulations. Journal of Computer-Based Instruction, 16(1), 1–10.

    Google Scholar 

  • Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21(1), 1–29. doi:10.1207/s15516709cog2101_1.

    Article  Google Scholar 

  • Renkl, A. (2002). Worked-out examples: instructional explanations support learning by self-explanations. Learning and Instruction, 12(5), 529–556.

    Article  Google Scholar 

  • Rummelhart, D. E., & Ortony, A. (1977). The representation of knowledge in memory. In R. C. Anderson, R. J. Spiro, & W. E. Montague (Eds.), Schooling and the acquisition of knowledge. Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Salomon, G. (1981). Communication and education: Social and psychological interactions. Beverly Hills, CA: Sage Publications.

    Google Scholar 

  • Schommer, M., & Surber, J. R. (1986). Comprehension-monitoring failure in skilled adult readers. Journal of Educational Psychology, 78(5), 353–357. doi:10.1037/0022-0663.78.5.353.

    Article  Google Scholar 

  • Schraw, G. (2009). Measuring metacognitive judgments. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 415–429). New York: Routledge.

    Google Scholar 

  • Sweller, J. (1999). Instructional design in technical areas. Camberwell, VIC: The Australian Council for Educational Research Ltd.

    Google Scholar 

  • Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123–138. doi:10.1007/s10648-010-9128-5.

    Article  Google Scholar 

  • Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. New York: Springer.

    Book  Google Scholar 

  • Thiede, K. W., & Anderson, M. C. M. (2003). Summarizing can improve metacomprehension accuracy. Contemporary Educational Psychology, 28(2), 129–160. doi:10.1016/S0361-476X(02)00011-5.

    Article  Google Scholar 

  • Tuovinen, J. E., & Sweller, J. (1999). A comparison of cognitive load associated with discovery learning and worked examples. Journal of Educational Psychology, 91(2), 334–341. doi:10.1037/0022-0663.91.2.334.

    Article  Google Scholar 

  • Van Loon, M. H., de Bruin, A. B. H., van Gog, T., & van Merrienboer, J. J. G. (2013). Activation of inaccurate prior knowledge affects primary-school students’ metacognitive judgments and calibration. Learning and Instruction, 24, 15–25.

    Article  Google Scholar 

  • Van Loon, M. H., de Bruin, A. B. H., van Gog, T., van Merrienboer, J. J. G., & Dunlosky, J. (2014). Can students evaluate their understanding of cause-and-effect relations? The effects of diagram completion on monitoring accuracy. Acta Psychologica, 151, 143–154.

    Article  Google Scholar 

  • Winne, P. H. (2004). Students’ calibration of knowledge and learning processes: Implications for designing powerful software learning environments. International Journal of Educational Research, 41, 466–488.

    Article  Google Scholar 

  • Winne, P. H., & Hadwin, A. F. (1998). Studying as self-regulated learning. In J. Dunlosky & A. C. Graesser (Eds.), Metacognition in educational theory and practice (pp. 277–304). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Wittrock, M. C. (1974). Learning as a generative process. Educational Psychologist, 19(2), 87–95. doi:10.1080/00461520903433554.

    Article  Google Scholar 

  • Wittrock, M. C. (1979). The cognitive movement in instruction. Educational Researcher, 8(2), 5–11. doi:10.3102/0013189X008002005.

    Article  Google Scholar 

  • Wittrock, M. C. (1989). Generative processes of comprehension. Educational Psychologist, 24, 345–376. doi:10.1207/s15326985ep2404_2.

    Article  Google Scholar 

  • Wittrock, M. C., & Alesandrini, K. (1990). Generation of summaries and analogies and analytic and holistic abilities. American Educational Research Journal, 27(3), 489–502.

    Article  Google Scholar 

  • Zimmerman, B. J. (1986). Becoming a self-regulated learner: Which are the key subprocesses? Contemporary Educational Psychology, 11(4), 307–313. doi:10.1016/0361-476X(86)90027-5.

    Article  Google Scholar 

  • Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts & P. R. Pintrich (Eds.), Handbook of self-regulation (pp. 13–39). New York: Academic Press.

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center for Research and Reform in Education, Johns Hopkins University, 300 E. Joppa Rd, Baltimore, MD, 21286, USA

    Jennifer R. Morrison & Steven M. Ross

  2. Darden College of Education, Old Dominion University, 218 Education Building, Norfolk, VA, 23529, USA

    Linda Bol & Ginger S. Watson

Authors
  1. Jennifer R. Morrison
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Linda Bol
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Steven M. Ross
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ginger S. Watson
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jennifer R. Morrison.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Morrison, J.R., Bol, L., Ross, S.M. et al. Paraphrasing and prediction with self-explanation as generative strategies for learning science principles in a simulation. Education Tech Research Dev 63, 861–882 (2015). https://doi.org/10.1007/s11423-015-9397-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11423-015-9397-2

Keywords

Search

Navigation